Outrunning Time’s Arrow – How to download experience?

Friday 14 September 2018

Control of our flaws is key to career success in the Machine Learning age. In the same way that the invention of the Sat-Nav has made car journeys easier (and saved many a marriage), a digital assistant recording our actions, contextualising them and revealing our behavioural quirks to ourselves, can speed up our personal development. Neiloy Ghosh, CFA and director, client services at Inalytics tells us more about what the future could look like for investment professionals.

There is no doubt that the best investors exhibit high levels of skill in their core activities of stock selection and position management and thus tend to enjoy career success and longevity. However, differentiating between luck and skill in the face of stellar short term results has long been a tricky task for investment firms, asset owners and even the investors themselves.

A run of good results can easily be comparable to the “hot hands” phenomenon in basketball. However unless those results have been generated by a good decision-making process, sensible assumptions and sound judgement, the powerful force of mean reversion is likely to triumph. As the writer John Ruskin stated: “skill is the unified force of experience, intellect and passion in their operation”. Experience is a key component of skill but, unfortunately for investment professionals starting out on their career journey, the only way to accumulate it is through time in the market observing, thinking, and acting.

A major part of accumulating experience is learning from errors. Indeed, without making errors we cannot really claim to be highly experienced. Yet, this key aspect of accumulating experience (and subsequently acquiring skill) is the very thing that investment professionals are currently threatened by. Behavioural finance theory constantly reminds us that not only are we error prone but predictably and repetitively so. Today we also have Machine Learning programmes presented as an existential threat to human investors but they too accumulate experience in real-time, through trial and painful error. As humans, we are not at a disadvantage because we make mistakes but because we all too frequently fail to acknowledge, admit and learn from them. Successful investing requires humility and an ongoing personal objective of minimising or swiftly recognising errors but, given our human frailties, can we hope to do this better than the machines?

For investment professionals at the outset or middle of their careers, a solution is likely to present itself in the near future and technology is our ally not our opponent. In the same way that the invention of the Sat-Nav has made car journeys easier (and saved many a marriage), a digital assistant recording our actions, contextualising them and revealing our behavioural quirks to ourselves, can speed up our personal development. The first digital assistants are already spreading through society so how long before we can work alongside an Investment Alexa? Thus as we accumulate experience we will be able to benefit from permanent and comprehensive digital records as opposed to fragile and self-serving memories.

We will be paid in the future based on how well we work with our digital colleagues and there will be a blurred line between what we do and they do. These relationships may turn out to be as fraught as our human ones as we will have to accept our colleague constantly exposing the flaws in our reasoning – those behavioural quirks – and providing evidence through past examples and probability based analysis. A bit like this exchange:

C-3PO: Sir, the possibility of successfully navigating an asteroid field is approximately three thousand, seven hundred and twenty to one!

Control of our flaws is key to career success in the Machine Learning age. Machine solutions are ideal for a corporate world where reducing uncertainty is imperative to efficiency. For investors however, uncertainty is intrinsic to what we do and perhaps our greatest skill is navigating in the fog. Experience teaches us that our reasoning is frequently flawed and that our certainty can rarely be absolute. In time the machines may learn the same truth but until that time our humanity can still provide us with a career edge.